This blog includes frequently asked Data Science questions. This article will give a glimpse to enhance all the concepts necessary to clear the interviews.
After some basic Data Science interview questions, we have included some technical and Data analysis questions that further help you crack an interview.
Most Asked Data Science Interview Questions
1. What is Data Science? How it is different from Big Data?
Data Science is an interdisciplinary field that blends several tools, algorithms, and machine learning principles to with the aim to find common patterns and assemble realistic insights from the raw data using mathematical and statistical approach is called Data Science.
How Data Science is different from Big Data?
| Data Science | Big Data |
| Data Science is popular in the field of digital advertising, recommendation systems (Amazon, Facebook, and Netflix) and handwriting recognition sectors. | Common applications are in the sector of communication, purchase and sale of goods, educational and financial fields. |
| Data Science exploits statistical and machine learning algorithms to procure accurate predictions from raw data. | Big Data decodes issues related to data management and handling, and analyze insights resulting in good decision making. |
| Data Science popular tools are Python, SAS, R, SQL etc. | Big Data popular tools are Spark, Hadoop, Hive, Flink etc. |
2. List the major differences between Supervised and Unsupervised Learning?
| Supervised Learning | Unsupervised Learning |
| Input data used is labelled and known. | Input data used unlabelled. |
| This approach is utilized for prediction. | This approach used for analysis. |
| Frequently used supervised learning algorithms include decision trees, Neural Networks,logistic regression and support vector machine. | The most commonly used algorithms include Anomaly Detection, Latent Variable Models, clustering. |
| Enables classification and regression. | Enables Classification, density estimation, dimension reduction. |
Read: Who is a Data Scientist, a Data Analyst and a Data Engineer
3. How Data Analytics is different from Data Science?
- Data Science is responsible for transforming data with the help of various technical analysis approaches to exploit required insights using which data analyst employ to thier different business solutions.
- Data Analytics involves the task of examining the existing hypothesis and information and helps in answering the questions to provide effective business related decision making process.
4. Mention some of the techniques used for sampling.
It is highly challenging task to conduct Data analysis on a whole volume of data at a time specifically when it includes larger datasets.
It becomes essential to collect some data samples that could be used for illustrating the whole population and later carry out analysis on it.
Notably, there are two different methods of sampling techniques based on the utilization of statistical models.
1. Probability Sampling Techniques:
- Clustered sampling.
- Simple random sampling.
- Stratified sampling.
2. Non-probability Sampling Techniques:
- Quota sampling.
- Snowball sampling.
- Convenience sampling etc.
5. Brief the steps involved in making a decision tree.
Making decision tree includes the following steps:
1. Get the list of entire dataset as input which are helpful for making a decision tree.
2. Evaluate entropy of the target variable and predictor attributes.
3. Evaluate the information gain of total attributes.
4. Select the attribute along with the highest information gain as the root node.
5. Reiterate the same approach on each branch until the decision node of every branch is concluded.
6. How Data Scientists check for data quality?
Some of the terms utilized to check data quality:
Integrity.
Uniqueness.
Accuracy.
Consistency.
Completeness.
7. Explain in brief about Hadoop.
Hadoop is a an open-source processing platform that handles data processing and storage for big data applications built on pooled systems.
Hadoop handles the task of splitting files into separate large blocks and directs them across nodes in a cluster. It then shifts a packs of code to nodes to execute the data in parallel.
8. What is the abbreviation of ‘fsck’?
‘fsck’ abbreviation stands for ‘file system check’. It performs handling the task of searching for possible errors in the file.
9. What are the conditions for Overfitting and Underfitting?
Overfitting: The Overfitting model process the simple training data. Incase any new data employed as input to the model, it fails to give any output. These conditions result owing to low bias and high variance in the model. Decision trees are more vulnerable to overfitting.
Underfitting: In underfitting, the model will be so simple that it is fails to find out the exact relationship in the data, and hence it does not execute well on the test data. This can take place due to high bias and low variance. Linear regression is more vulnerable to underfitting.
10. Explain about Recommender systems?
Recommender systems are a subdivision of information filtering systems, utilized to analyse how consumers would rate particular objects such as music, movies and more.
Recommender systems filter large filter huge chunk of information based on the data fecilitated by a user and other factors, and they also manage user’s preference and interest.
11. Explain differences between wide and long data formats.
Categorical data are always grouped in a wide format.
The long format is in which there are a number of instances with several instances with many variables and subject variables.
12. How much data is required to get a valid outcome?
All the industries are different and evaluate in different ways. Thus, they never have enough data. The amount of data which is essential depends on the approaches users use to have an best chance of procuring vital results.
13. Explain Eigen values and Eigen vectors.
Eigenvectors are known as unit vectors or colomn vectors whose length to magnitude ratio is 1.
Eigenvalues are coefficients that are implied on eigenvectors that assign these vectors different values for length or magnitude.
14. Explain about power analaysis.
Power analysis enables the determination of the sample size essential to find out an effect of a given siz with a assigned degree of confidence.
15. Explain logistic regression. Mention any example related to logistic regression.
Logisti regression is also called as the logit model. It is a approach to forecast outcome from a linear combination of variables.
For instance, let’s say that we would like to forecast the outcome of elections for a specific political leader. Therefore, we need to search whether this politician has the potential to win the election or not. Hence, the outcome would be binary that is win (1) or loss (0).
16. Exaplain Linear Regression. Mention some of the disadvantages of the linear model.
Linear regression is an approach in which the score of variable Y is calculated with the help of a predictor variable X. Y is known as the criterion variable.
Some of the disadvantages of Linear Regression are:
- The assumption of linearity of errors is a major setback.
- Overfitting problems are present which are difficult to solve.
17. Explain Random forest model and steps to build it.
A random forest is created with the help of many number of decision trees. If you distribute the data into several different packages and build a decision tree in each of the different groups of data, the Random forest includes all those trees together.
Steps to create a random forest model:
1. Randomly choose ‘k’ features from the sum of ‘m’ features provided k<<m.
2. Out of the ‘k’ features, predict the node D with the help of split point.
3. Use the best split to divide the node into daughter nodes.
4. Reiterate the steps two and three until leaf nodes are conirmed.
5. Build the Random forest model by reiterating the steps one to four for ‘n’ times to build ‘n’ number of trees.
18. Explain in brief about Neural Network Fundamentals.
A Neural network is an artificial representation of the human brain that attempts to simulate its learning process. The neural network understands the patterns from the data and utilizes the the information that it acquires to predict the output of new data, with no human assistance.
19. Explain about auto-encoders.
Auto-encoders are called as learning networks. They ensure minimum possible errors while transforming inputs into outputs. Therefore, Auto-encoders tries to confirm if required output is equal or as close as to input.
20. Explain root cause analysis?
Root cause analysis initially designed with motive to analyse industrial accidents. It is basically a problem-solving method utilized for isolating the root causes of problems or faults.